Machine learning apparatus and method based on multi-feature extraction and transfer learning, and leak detection apparatus using the same

Abstract

An apparatus/method for extracting multiple features from time series data collected from a plurality of sensors and for performing transfer learning on them. There is provided an apparatus including: a multi-feature extraction unit for extracting multiple features from a data stream for each sensor inputted from the plurality of sensors; a transfer-learning model generation unit for extracting useful multi-feature information from a learning model which has finished pre-learning, for the multiple features for forwarding the extracted multi-feature information to a multi-feature learning unit to generate a learning model that performs transfer learning on the multiple features; and the multi-feature learning unit for receiving learning variables from the learning model for each of the multiple features and for performing parallel learning for the multiple features, to calculate and output a loss. In addition, there is provided an apparatus for detecting leaks in plant pipelines.

What is claimed is: 
     
         1 . A machine learning apparatus based on multi-feature extraction and transfer learning from data streams transmitted from a plurality of sensors, comprising:
 a multi-feature extraction unit for extracting multiple features from a data stream for each sensor inputted from the plurality of sensors, wherein the multiple features comprise ambiguity features that have been ambiguity-transformed from characteristics of the input data and multi-trend correlation features extracted for each of multiple trend intervals according to a number of packet intervals constituting the data stream for each sensor;   a transfer-learning model generation unit for extracting useful multi-feature information from a learning model which has finished pre-learning for the multiple features and for forwarding the extracted multi-feature information to a multi-feature learning unit below, so as to generate a learning model that performs transfer learning for each of the multiple features; and   a multi-feature learning unit for receiving learning variables from the learning model for each of the multiple features and for performing parallel learning for the multiple features, so as to calculate and output a loss.   
     
     
         2 . The apparatus of  claim 1 ,
 wherein the multi-feature extraction unit comprises an ambiguity feature extractor,   wherein the ambiguity feature extractor is configured to convert characteristics in a form of sensor data from the data stream transmitted from each of the sensors into an image feature through ambiguity transformation using the cross time-frequency spectral transformation and the 2D Fourier transformation.   
     
     
         3 . The apparatus of  claim 2 , wherein the ambiguity features comprise a three-dimensional volume feature generated by accumulating two-dimensional features in a depth direction. 
     
     
         4 . The apparatus of  claim 1 ,
 wherein the multi-feature extraction unit comprises a multi-trend correlation feature extractor for extracting the multi-trend correlation features,   wherein the multi-trend correlation feature extractor is configured to construct column vectors with data extracted during multiple trend intervals consisting of different numbers of packet intervals in the data stream for each sensor, and to extract data for each trend interval so that sizes of the column vectors for each trend interval are the same, so as to output the multi-trend correlation features.   
     
     
         5 . The apparatus of  claim 1 ,
 wherein the learning model generated in the transfer-learning model generation unit comprises a teacher model for extracting and forwarding information which has finished pre-learning and a student model for receiving the extracted information,   wherein the student model is configured in the same number as the multiple features, and the useful information of the teacher model that has finished pre-learning is forwarded to a plurality of student models for the multiple features so as to be learned.   
     
     
         6 . The apparatus of  claim 1 ,
 wherein the learning model generated in the transfer-learning model generation unit comprises a teacher model for extracting and forwarding information which has finished pre-learning and a student model for receiving the extracted information,   wherein the student model is configured as a single common model, and the useful information of the teacher model that has finished pre-learning is forwarded to the single common student model so as to be learned.   
     
     
         7 . The apparatus of  claim 5 ,
 wherein the useful information extracted from the teacher model is a single piece of hint information corresponding to an output of feature maps comprising learning variable information from a learning data input to any layer,   wherein forwarding of this single piece of hint information is performed such that a loss function for the Euclidean distance between an output result of feature maps at a layer selected from the teacher model and an output result of feature maps at a layer selected from the student model is minimized.   
     
     
         8 . The apparatus of  claim 6 ,
 wherein the useful information extracted from the teacher model is a single piece of hint information corresponding to an output of feature maps comprising learning variable information from a learning data input to any layer,   wherein forwarding of this single piece of hint information is performed such that a loss function for the Euclidean distance between an output result of feature maps at a layer selected from the teacher model and an output result of feature maps at a layer selected from the student model is minimized.   
     
     
         9 . The apparatus of  claim 1 , further comprising a means for updating the learning model generated in the transfer-learning model generation unit. 
     
     
         10 . The apparatus of  claim 1 , wherein the means for updating the learning model is performed when in any one case among:
 if there is a change in a distribution of the data collected, and if a distribution of the data collected departs from a range defined by the user.   
     
     
         11 . The apparatus of  claim 1 , further comprising a multi-feature evaluation unit for finally evaluating learning results by receiving results that have been learned from the multi-feature learning unit. 
     
     
         12 . The apparatus of  claim 11 , further comprising a multi-feature combination and optimization unit for repetitively performing combination of the multiple features until an optimal combination of the multiple features according to a loss is acquired based on the learning results inputted in the multi-feature evaluation unit. 
     
     
         13 . A machine learning method based on multi-feature extraction and transfer learning from data streams transmitted from a plurality of sensors, comprising steps of:
 extracting multiple features from a data stream for each sensor inputted from the plurality of sensors, wherein the multiple features comprise ambiguity features that have been ambiguity-transformed from characteristics of the input data and multi-trend correlation features extracted for each of multiple trend intervals according to a number of packet intervals constituting the data stream for each sensor;   generating a transfer-learning model for extracting useful multi-feature information from a learning model which has finished pre-learning for the multiple features and for forwarding the extracted multi-feature information to a multi-feature learning procedure below, so as to generate a learning model that performs transfer learning for each of the multiple features; and   learning multiple features for receiving learning variables from the learning model for each of the multiple features and for performing parallel learning for the multiple features, so as to calculate and output a loss.   
     
     
         14 . The method of  claim 13 ,
 wherein the multi-feature extraction step comprises a step of extracting ambiguity features,   wherein the step of extracting the ambiguity features is configured to convert characteristics in a form of sensor data from the data stream transmitted from each of the sensors into an image feature through ambiguity transformation using the cross time-frequency spectral transformation and the 2D Fourier transformation.   
     
     
         15 . The method of  claim 14 , wherein the ambiguity feature comprise a three-dimensional volume feature generated by accumulating two-dimensional features in a depth direction. 
     
     
         16 . The method of  claim 13 ,
 wherein the step of extracting multi-feature comprises a step of extracting multi-trend correlation feature,   wherein the multi-trend correlation feature extraction step is configured to construct column vectors with data extracted during multiple trend intervals having different numbers of packet intervals in the data stream for each sensor, and to extract data for each trend interval so that sizes of the column vectors for each trend interval are the same, so as to output the multi-trend correlation features.   
     
     
         17 . The method of  claim 13 , further comprising a step of periodically updating the learning models generated in the transfer-learning model generation step. 
     
     
         18 . The method of  claim 13 , further comprising a step of evaluating a multi-feature for finally evaluating learning results by receiving results that have been learned from the multi-feature learning step. 
     
     
         19 . The method of  claim 18 , further comprising a step of combining and optimizing multiple features for repetitively performing combination of the multiple features until an optimal combination of the multiple features according to a loss is acquired based on the learning results inputted in the multi-feature evaluation procedure. 
     
     
         20 . An apparatus for detecting fine leaks using a machine learning apparatus based on multi-feature extraction and transfer learning from data streams transmitted from a plurality of sensors, comprising:
 a multi-feature extraction unit for extracting multiple features from a data stream for each sensor inputted from the plurality of sensors, wherein the multiple features comprise ambiguity features that have been ambiguity-transformed from characteristics of the input data and multi-trend correlation features extracted for each of multiple trend intervals according to a number of packet intervals constituting the data stream for each sensor;   a transfer-learning model generation unit for extracting useful information from a learning model which has finished pre-learning for the multiple features, for forwarding the extracted useful information to a multi-feature learning unit below so as to generate a learning model that performs transfer learning for each of the multiple features;   a multi-feature learning unit for receiving learning variables from the learning model for each of the multiple features and for performing parallel learning for the multiple features, so as to calculate and output a loss; and   a multi-feature evaluation unit for finally evaluating whether there is a fine leak by receiving results that have been learned from the learning model generated in the multi-feature learning unit.